Krina Mehta, Principal Scientist
Krina Mehta is a translational and quantitative pharmacologist with over 15 years of industry experience advancing model-informed drug development (MIDD) strategies across therapeutic areas and modalities. Her work bridges biology, pharmacology, mathematics, and technology to enable efficient and science-driven development of safe and effective medicines.
She is deeply passionate about modernizing preclinical drug development through New Approach Methodologies (NAMs). Her vision is to strategically integrate NAMs—such as organ-on-chip systems, in silico models, and mechanistic simulations—into translational science frameworks to make drug development more efficient, ethical, and data-driven.
She currently serve as Director of Quantitative Pharmacology at Enveda, where she lead quantitative strategies to support preclinical-to-clinical translation and decision-making. She holds a PhD in Quantitative Pharmacology from Leiden University (The Netherlands), an MS in Pharmacometrics from the University of Maryland (USA), and a BPharm from Saurashtra University (India).
If you share an interest in quantitative modeling, NAMs, or translational pharmacology, follow us, connect with us, and share your NAM ideas and relevant work.
Akshat Shah
Akshat Shah is an applied machine learning and AI engineer with four years of experience building intelligent software systems for real-world business and research applications. His work bridges machine learning, software engineering, data architecture, and business strategy to enable organizations to deploy AI that holds up in production, not just in a controlled test environment.
He is deeply passionate about making AI practically useful in data-intensive fields through production ML engineering. His project work spans predictive modeling pipelines, intelligent extraction and classification systems for complex multi-format datasets, and automated research workflows that reduce the manual overhead of working with large volumes of structured and unstructured data. His vision is to build the technical infrastructure that makes AI a reliable tool in scientific and operational environments, not an experiment that stalls before it reaches the people who need it.
He currently serves as CEO of Aivaura, an AI engineering studio where he leads the design and development of custom machine learning pipelines, natural language processing systems, intelligent document processing solutions, and AI backends built for operational use. He holds a B.Tech in Computer Science and Engineering from Nirma University (India).
